Multitask Matrix Completion for Learning Protein Interactions Across Diseases

  • Meghana Kshirsagar
  • Jaime G. Carbonell
  • Judith Klein-Seetharaman
  • Keerthiram Murugesan
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9649)

Abstract

Disease causing pathogens such as viruses, introduce their proteins into the host cells where they interact with the host’s proteins enabling the virus to replicate inside the host. These interactions between pathogen and host proteins are key to understanding infectious diseases. Often multiple diseases involve phylogenetically related or biologically similar pathogens. Here we present a multitask learning method to jointly model interactions between human proteins and three different, but related viruses: Hepatitis C, Ebola virus and Influenza A. Our multitask matrix completion based model uses a shared low-rank structure in addition to a task-specific sparse structure to incorporate the various interactions. We obtain upto a 39 % improvement in predictive performance over prior state-of-the-art models. We show how our model’s parameters can be interpreted to reveal both general and specific interaction-relevant characteristics of the viruses. Our code, data and supplement is available at: http://www.cs.cmu.edu/~mkshirsa/bsl_mtl.

Keywords

Host-pathogen protein interactions Multitask learning Matrix completion 
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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Meghana Kshirsagar
    • 1
  • Jaime G. Carbonell
    • 2
  • Judith Klein-Seetharaman
    • 3
  • Keerthiram Murugesan
    • 2
  1. 1.IBM T. J. Watson ResearchYorktown HeightsUSA
  2. 2.Language Technologies InstituteCarnegie Mellon UniversityPittsburghUSA
  3. 3.Metabolic and Vascular Health, Warwick Medical SchoolUniversity of WarwickCoventryUK

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